Understanding personal protective behaviours and opportunities for interventions:

Results from a multi-method investigation of cross-sectional data

Kaisa Sauro, James Twose, Gjalt-Jorn Peters, Matti Heino & Nelli Hankonen

approach used here: CatBoost Regression

import pandas as pd
import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt
import session_info

from sklearn.metrics import mean_squared_error
from sklearn.model_selection import KFold, GroupKFold, GroupShuffleSplit, RepeatedStratifiedKFold, RepeatedKFold

from sklearn.model_selection import train_test_split, GridSearchCV

from sklearn.model_selection import RepeatedStratifiedKFold, cross_val_score, cross_validate
from sklearn.ensemble import BaggingClassifier, BaggingRegressor

from sklearn.model_selection import KFold

import pingouin as pg

from catboost import CatBoostRegressor, Pool

# import xgboost
import shap
shap.initjs()

_ = sns.set_style("whitegrid")

Virtual Environments and Packages

session_info.show(req_file_name="corona_preppers-requirements.txt",
      write_req_file=False) #add write_req_file=True to function to get requirements.txt file of packages used

Click to view session information

-----
catboost            1.0.3
matplotlib          3.3.1
numpy               1.19.1
pandas              1.1.3
pingouin            0.5.0
seaborn             0.11.0
session_info        1.0.0
shap                0.39.0
sklearn             0.23.2
-----

Click to view modules imported as dependencies

PIL                 8.0.0
appnope             0.1.0
backcall            0.2.0
beta_ufunc          NA
binom_ufunc         NA
brotli              NA
certifi             2021.10.08
cffi                1.14.3
chardet             3.0.4
cloudpickle         1.6.0
colorama            0.4.4
cycler              0.10.0
cython_runtime      NA
dateutil            2.8.1
decorator           4.4.2
idna                2.10
ipykernel           5.3.4
ipython_genutils    0.2.0
jedi                0.18.1
joblib              0.17.0
kiwisolver          1.2.0
littleutils         NA
llvmlite            0.34.0
mpl_toolkits        NA
nbinom_ufunc        NA
numba               0.51.2
outdated            0.2.1
pandas_flavor       NA
parso               0.8.0
pexpect             4.8.0
pickleshare         0.7.5
pkg_resources       NA
prompt_toolkit      3.0.8
ptyprocess          0.6.0
pygments            2.7.1
pyparsing           2.4.7
pytz                2020.1
requests            2.24.0
scipy               1.7.1
six                 1.15.0
slicer              NA
socks               1.7.1
statsmodels         0.12.0
storemagic          NA
tabulate            0.8.7
threadpoolctl       2.1.0
tornado             6.0.4
tqdm                4.50.2
traitlets           5.0.5
urllib3             1.25.11
wcwidth             0.2.5
xarray              0.16.1
zmq                 19.0.2

-----
IPython             7.18.1
jupyter_client      6.1.7
jupyter_core        4.6.3
jupyterlab          2.2.6
notebook            6.1.4
-----
Python 3.8.5 (default, Sep  4 2020, 02:22:02) [Clang 10.0.0 ]
macOS-10.16-x86_64-i386-64bit
-----
Session information updated at 2021-12-10 13:03

Read in data, show info and data head

df = pd.read_csv("data/shield_gjames_21-09-20_prepped.csv").drop("Unnamed: 0", axis=1)

df.head()

	id	sampling_weight	demographic_gender	demographic_age	demographic_4_areas	demographic_8_areas	demographic_higher_education	behaviour_indoors_nonhouseholders	behaviour_close_contact	behaviour_quarantined	...	intention_public_transport_recoded	intention_indoor_meeting_recoded	intention_restaurant_recoded	intention_pa_recoded	intention_composite	behaviour_indoors_nonhouseholders_recoded	behaviour_unmasked_recoded	behavior_composite	behavior_composite_recoded	intention_behavior_composite
0	1	2.060959	2	60+	2	7	0	2	5	2	...	0	0	0	0	0	1.000000	0.000000	0.000000	0.000000	0.000000
1	2	1.784139	2	40-49	1	1	1	3	3	2	...	0	1	1	1	3	0.785714	0.214286	0.168367	0.841837	1.920918
2	3	1.204000	1	60+	1	2	1	4	4	2	...	0	0	0	0	0	0.500000	0.214286	0.107143	0.535714	0.267857
3	4	2.232220	1	60+	2	6	0	4	3	2	...	0	2	0	2	4	0.500000	0.500000	0.250000	1.250000	2.625000
4	5	1.627940	2	18-29	1	3	0	6	3	2	...	0	2	0	0	2	0.000000	0.214286	0.000000	0.000000	1.000000

5 rows × 106 columns

sdt_columns = df.filter(regex="sdt").columns.tolist()

drop_sdt = True
if drop_sdt:
    df=df.drop(sdt_columns, axis=1)

df.shape

(2272, 87)

Specify the feature list, grouping variable, and specify the grouping variable as a categorical variable

target = "intention_behavior_composite"

df[target] = (df[target] - 10) * -1

features_list = df.filter(regex="^automaticity|attitude|^norms|^risk|^effective").columns.tolist()

meta_df = pd.read_excel("metadata/questionLabels.xlsx").iloc[:, 0:7]

meta_list = df.filter(regex="^automaticity|attitude|^norms|^risk|^effective|^behaviour|^intention").columns.tolist()

pd.set_option("display.max_colwidth", 350)
pd.set_option('display.expand_frame_repr', True)
meta_df.loc[meta_df["New variable name"].isin(meta_list), ["Item english translation", "New variable name"]]

	Item english translation	New variable name
12	How often in the last 7 days have you been indoors with people outside your household so that it is not related to obligations? For example, meeting friends, visiting hobbies, non-essential shopping, or other activities that are not required for your work or other duties.	behaviour_indoors_nonhouseholders
13	In the last 7 days, have you been in close contact with people outside your household? Direct contact means spending more than one minute less than two meters away from another person or touching (e.g., shaking hands) outdoors or indoors.	behaviour_close_contact
14	Are you currently in quarantine or isolation due to an official instruction or order? (For example, because you are waiting for a corona test, have returned from abroad or been exposed to a coronavirus)	behaviour_quarantined
15	How often in the last 7 days were you in your free time without a mask indoors with people you don’t live with?	behaviour_unmasked
24	If in the next 7 days you go to visit the following indoor spaces and there are people outside your household, Are you going to wear a mask? Grocery store or other store	intention_store
25	If in the next 7 days you go to visit the following indoor spaces and there are people outside your household, Are you going to wear a mask? Bus, train or other means of public transport	intention_public_transport
26	If in the next 7 days you go to visit the following indoor spaces and there are people outside your household, Are you going to wear a mask? Meeting people outside your household indoors	intention_indoor_meeting
27	If in the next 7 days you go to visit the following indoor spaces and there are people outside your household, Are you going to wear a mask? Cafe, restaurant or bar indoors	intention_restaurant
28	If in the next 7 days you go to visit the following indoor spaces and there are people outside your household, Are you going to wear a mask? Indoor exercise	intention_pa
29	Taking a mask with you to a store or public transport, for example, has already become automatic for some and is done without thinking. For others, taking a mask with them is not automatic at all, but requires conscious thinking and effort.	automaticity_carry_mask
30	Putting on a mask, for example in a shop or on public transport, has already become automatic for some and it happens without thinking. For others, putting on a mask is not automatic at all, but requires conscious thinking and effort.	automaticity_put_on_mask
32	What consequences do you think it has if you use a face mask in your free time? If or when I use a face mask…	inst_attitude_protects_self
33	What consequences do you think it has if you use a face mask in your free time? If or when I use a face mask…	inst_attitude_protects_others
34	What consequences do you think it has if you use a face mask in your free time? If or when I use a face mask…	inst_attitude_sense_of_community
35	What consequences do you think it has if you use a face mask in your free time? If or when I use a face mask…	inst_attitude_enough_oxygen
36	What consequences do you think it has if you use a face mask in your free time? If or when I use a face mask…	inst_attitude_no_needless_waste
37	Who thinks you should use a face mask and who thinks not? In the following questions, by using a face mask, we mean holding a cloth or disposable face mask, surgical mask, or respirator on the face so that it covers the nose and mouth. The questions concern leisure time. My family and friends think I should ..	norms_family_friends
38	People at risk think I should ..	norms_risk_groups
39	The authorities think I should ..	norms_officials
40	In the indoors spaces I visit, people on the site think I should…	norms_people_present_indoors
41	When I use a face mask, I feel or would feel ...	aff_attitude_comfortable
42	When I use a face mask, I feel or would feel ...	aff_attitude_calm
43	When I use a face mask, I feel or would feel ...	aff_attitude_safe
44	When I use a face mask, I feel or would feel ...	aff_attitude_responsible
45	When I use a face mask, I feel or would feel ...	aff_attitude_difficult_breathing
61	If two unvaccinated people from different households meet indoors, what means do you think would be effective in preventing coronavirus infection? Hand washing and use of gloves	effective_means_handwashing
62	Using a face mask	effective_means_masks
63	Keeping a safety distance (2 meters)	effective_means_distance
64	Ventilation	effective_means_ventilation
65	How likely do you think you will get a coronavirus infection in your free time in the next month?	risk_likely_contagion
66	How likely do you think you would get a coronavirus infection in your free time in the next month if you did nothing to protect yourself from it?	risk_contagion_absent_protection
67	If you got a coronavirus infection, how serious a threat would you rate it to your health?	risk_severity
68	Spread of coronavirus…	risk_fear_spread
69	The fact that I would get infected myself ..	risk_fear_contagion_self
70	That my loved one would get infected...	risk_fear_contagion_others
71	Consequences of measures taken to prevent the spread of the coronavirus...	risk_fear_restrictions

pd.set_option("display.max_colwidth", 100)

EDA on the target

Check the amount of samples in the target

_ = sns.violinplot(data=df[[target]].melt(), 
                    x="variable", 
                    y="value"
               )
_ = sns.stripplot(data=df[[target]].melt(), 
                    x="variable", 
                    y="value",
                  edgecolor='white',
                  linewidth=0.5
               )

pd.crosstab(df["demographic_gender"], df["demographic_age"])

demographic_age	18-29	30-39	40-49	50-59	60+
demographic_gender
1	114	169	187	168	337
2	281	185	229	211	391

target_df = df[target]
target_df.describe().to_frame().T

	count	mean	std	min	25%	50%	75%	max
intention_behavior_composite	2272.0	8.582428	1.524704	-0.0	8.017857	8.964286	9.5	10.0

_ = plt.figure(figsize=(20, 5))
_ = sns.countplot(x=target_df)
_ = plt.xticks(rotation=90)

Force all feature variables to categorical data

df[features_list] = df[features_list].astype("category")

df = (df[["demographic_age", "demographic_higher_education"] + features_list + [target]])

df.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 2272 entries, 0 to 2271
Data columns (total 30 columns):
 #   Column                            Non-Null Count  Dtype   
---  ------                            --------------  -----   
 0   demographic_age                   2272 non-null   object  
 1   demographic_higher_education      2272 non-null   int64   
 2   automaticity_carry_mask           2272 non-null   category
 3   automaticity_put_on_mask          2272 non-null   category
 4   inst_attitude_protects_self       2272 non-null   category
 5   inst_attitude_protects_others     2272 non-null   category
 6   inst_attitude_sense_of_community  2272 non-null   category
 7   inst_attitude_enough_oxygen       2272 non-null   category
 8   inst_attitude_no_needless_waste   2272 non-null   category
 9   norms_family_friends              2272 non-null   category
 10  norms_risk_groups                 2272 non-null   category
 11  norms_officials                   2272 non-null   category
 12  norms_people_present_indoors      2272 non-null   category
 13  aff_attitude_comfortable          2272 non-null   category
 14  aff_attitude_calm                 2272 non-null   category
 15  aff_attitude_safe                 2272 non-null   category
 16  aff_attitude_responsible          2272 non-null   category
 17  aff_attitude_difficult_breathing  2272 non-null   category
 18  effective_means_handwashing       2272 non-null   category
 19  effective_means_masks             2272 non-null   category
 20  effective_means_distance          2272 non-null   category
 21  effective_means_ventilation       2272 non-null   category
 22  risk_likely_contagion             2272 non-null   category
 23  risk_contagion_absent_protection  2272 non-null   category
 24  risk_severity                     2272 non-null   category
 25  risk_fear_spread                  2272 non-null   category
 26  risk_fear_contagion_self          2272 non-null   category
 27  risk_fear_contagion_others        2272 non-null   category
 28  risk_fear_restrictions            2272 non-null   category
 29  intention_behavior_composite      2272 non-null   float64 
dtypes: category(27), float64(1), int64(1), object(1)
memory usage: 123.2+ KB

grouping_var = target

display(df[grouping_var].value_counts().head().to_frame()), df.shape[0], df[grouping_var].value_counts().head().sum()

	intention_behavior_composite
10.000000	424
9.500000	228
9.000000	187
8.885204	155
9.385204	112

(None, 2272, 1106)

def naive_catboost_forest_summary(df: pd.DataFrame,
                                 grouping_var: str,
                                 column_list: list,
                                  plot_title: str
                                 ):
    y = df[grouping_var]
    X = df[column_list]

    feature_plot, ax = plt.subplots(figsize=(10,7))
    _ = sns.boxplot(ax=ax, 
                    data=X.apply(lambda x: x.cat.codes), 
                    orient="v", 
                   )
    _ = plt.title(f'Feature Distributions {plot_title}')
    _ = plt.setp(ax.get_xticklabels(), rotation=90)
    _ = plt.grid()
    _ = plt.tight_layout()
    _ = plt.show()

    model = CatBoostRegressor(iterations=500,
                               depth=None,
                               learning_rate=1,
                               loss_function='RMSE',
                               verbose=False)

    # train the model
    _ = model.fit(X, y, cat_features=column_list)

    # create dataframe with importances per feature
    feature_importance = pd.Series(dict(zip(column_list, model.feature_importances_.round(2)))) 

    feature_importance_df = pd.DataFrame(feature_importance.sort_values(ascending=False)).reset_index().rename(columns={"index": "feature", 0: "feature_importance"})

    _ = plt.figure(figsize=(7, 7))
    gini_plot = sns.barplot(data=feature_importance_df, 
                            x="feature_importance", 
                            y="feature")
    _ = plt.title(f'Feature Importance {plot_title}')
    _ = plt.show()

    shap_values = model.get_feature_importance(Pool(X, label=y,cat_features=X.columns.tolist()), type="ShapValues")

    shap_values = shap_values[:,:-1]

    _ = shap.summary_plot(shap_values, 
                                  X.astype(int), 
                                  feature_names=X.columns, 
                                  max_display=X.shape[1],
                                 show=False,
                         title=plot_title) 
    shap_plot = plt.gca()
    
    tmp_actual = (X
     .melt(value_name='actual_value')
    )

    tmp_shap = (pd.DataFrame(shap_values, columns=column_list)
     .melt(value_name='shap_value')
    )

    shap_actual_df = pd.concat([tmp_actual, tmp_shap[["shap_value"]]], axis=1)

    
    y_pred = model.predict(X)

    df_test = pd.DataFrame({"y_pred": y_pred, grouping_var: y})

    user_ids_first = df_test.head(1).index.tolist()[0]
    user_ids_last = df_test.tail(1).index.tolist()[0]

    _ = plt.figure(figsize=(30,8))
    _ = plt.title(f"Catboost Regressor(fitted set) | RMSE = {round(np.sqrt(mean_squared_error(df_test['y_pred'], df_test[grouping_var])),4)} | bias Error = {round(np.mean(df_test['y_pred'] - df_test[grouping_var]), 4)} | {plot_title}")
    rmse_plot = plt.stem(df_test.index, df_test['y_pred'] - df_test[grouping_var], use_line_collection=True, linefmt='grey', markerfmt='D')
    _ = plt.hlines(y=round(np.sqrt(mean_squared_error(df_test['y_pred'], df_test[grouping_var])),2), colors='b', linestyles='-.', label='+ RMSE', 
                   xmin = user_ids_first, 
                   xmax = user_ids_last
                  ) 
    _ = plt.hlines(y=round(-np.sqrt(mean_squared_error(df_test['y_pred'], df_test[grouping_var])),2), colors='b', linestyles='-.', label='- RMSE', 
                   xmin = user_ids_first, 
                   xmax = user_ids_last
                  ) 
    _ = plt.xticks(rotation=90, ticks=df_test.index)
    _ = plt.ylabel(f"'Error = y_predicted - {grouping_var}'")
    _ = plt.legend()
    _ = plt.show()
    
    return feature_plot, gini_plot.get_figure(), shap_plot.get_figure(), rmse_plot, feature_importance_df, shap_actual_df

# %%capture
feature_plot_0, gini_plot_0, shap_plot_0, rmse_plot_0, feature_importance_df_0, shap_values_0 = naive_catboost_forest_summary(df = df[df["demographic_age"].isin(['18-29', '30-39'])],
                                                                                   grouping_var = grouping_var,
                                                                                   column_list = features_list,
                                                                                                                                   plot_title="18 - 39"
                                                                                  )

feature_plot_1, gini_plot_1, shap_plot_1, rmse_plot_1, feature_importance_df_1, shap_values_1 = naive_catboost_forest_summary(df = df[df["demographic_age"].isin(['40-49', '50-59'])],
                                                                                   grouping_var = grouping_var,
                                                                                   column_list = features_list,
                                                                                                                                   plot_title="40 - 59"
                                                                                  )

feature_plot_2, gini_plot_2, shap_plot_2, rmse_plot_2, feature_importance_df_2, shap_values_2 = naive_catboost_forest_summary(df = df[df["demographic_age"].isin(['60+'])],
                                                                                   grouping_var = grouping_var,
                                                                                   column_list = features_list,
                                                                                                                                   plot_title="60+"
                                                                                  )

feature_plot_3, gini_plot_3, shap_plot_3, rmse_plot_3, feature_importance_df_3, shap_values_3 = naive_catboost_forest_summary(df = df,
                                                                                   grouping_var = grouping_var,
                                                                                   column_list = features_list,
                                                                                                                                   plot_title="All"
                                                                                  )

feature_plot_4, gini_plot_4, shap_plot_4, rmse_plot_4, feature_importance_df_4, shap_values_4 = naive_catboost_forest_summary(df = df[df[grouping_var]!=10],
                                                                                   grouping_var = grouping_var,
                                                                                   column_list = features_list,
                                                                                                                                   plot_title="All - No 10's in target"
                                                                                  )

feature_plot_5, gini_plot_5, shap_plot_5, rmse_plot_5, feature_importance_df_5, shap_values_5 = naive_catboost_forest_summary(df = df[df["demographic_higher_education"]==0],
                                                                                   grouping_var = grouping_var,
                                                                                   column_list = features_list,
                                                                                                                                   plot_title="Lower Education"
                                                                                  )

feature_plot_6, gini_plot_6, shap_plot_6, rmse_plot_6, feature_importance_df_6, shap_values_6 = naive_catboost_forest_summary(df = df[df["demographic_higher_education"]==1],
                                                                                   grouping_var = grouping_var,
                                                                                   column_list = features_list,
                                                                                                                                   plot_title="Higher Education"
                                                                                  )

fig, axs = plt.subplots(nrows=1,
                 ncols=4,
                 sharex=True,
                 sharey=False,
                       figsize=(30, 7),
                       gridspec_kw={'wspace': 0.75})
fi_dfs_list = [feature_importance_df_0, feature_importance_df_1, feature_importance_df_2, feature_importance_df_3]
fi_titles_list = ["18 - 39", "40 - 59", "60+", "All"]

for i in range(0, len(fi_dfs_list)):
    fi_df = fi_dfs_list[i]
    _ = sns.barplot(data=fi_df, 
                    x="feature_importance", 
                    y="feature",
                    ax=axs[i],
                    palette="rocket"
                    )
    _ = axs[i].set_title(fi_titles_list[i])
# _ = plt.show()

fi_dfs_list = [feature_importance_df_0, feature_importance_df_1, feature_importance_df_2, feature_importance_df_3]
fi_titles_list = ["18 - 39", "40 - 59", "60+", "All"]
for i in range(0, len(fi_dfs_list)):
    fi_dfs_list[i]["age_group"] = fi_titles_list[i]

_ = plt.figure(figsize=(7, 5))
_ = sns.barplot(
                data=pd.concat(fi_dfs_list, axis=0).groupby("age_group").head(5),
                x="feature_importance", 
                y="feature",
                hue="age_group",
                palette="rocket",
                dodge=True
                )

fis_df = pd.concat(fi_dfs_list, axis=1)

fis_df.head(5)

	feature	feature_importance	age_group	feature	feature_importance	age_group	feature	feature_importance	age_group	feature	feature_importance	age_group
0	automaticity_put_on_mask	12.42	18 - 39	automaticity_put_on_mask	19.44	40 - 59	automaticity_carry_mask	10.24	60+	automaticity_carry_mask	10.10	All
1	norms_people_present_indoors	7.77	18 - 39	inst_attitude_sense_of_community	11.25	40 - 59	norms_people_present_indoors	6.76	60+	risk_severity	7.71	All
2	aff_attitude_safe	6.97	18 - 39	inst_attitude_protects_self	5.38	40 - 59	aff_attitude_comfortable	6.18	60+	effective_means_masks	6.89	All
3	risk_fear_spread	6.38	18 - 39	effective_means_distance	4.80	40 - 59	risk_severity	5.80	60+	norms_people_present_indoors	5.15	All
4	inst_attitude_no_needless_waste	6.32	18 - 39	risk_fear_restrictions	4.63	40 - 59	aff_attitude_safe	5.61	60+	effective_means_ventilation	4.49	All

fig, axs = plt.subplots(nrows=1,
                 ncols=4,
                 sharex=True,
                 sharey=False,
                       figsize=(30, 7),
                       gridspec_kw={'wspace': 0.75})
shap_dfs_list = [shap_values_0, shap_values_1, shap_values_2, shap_values_3]
shap_titles_list = ["18 - 39", "40 - 59", "60+", "All"]

for i in range(0, len(shap_dfs_list)):
    shap_df = shap_dfs_list[i]
    var_order = shap_df.groupby("variable").var().sort_values(by = "shap_value", ascending = False).index.tolist()
    _ = sns.stripplot(data=shap_df, 
                    x="shap_value", 
                    y="variable",
                    hue="actual_value",
                  order=var_order,
                    ax=axs[i],
                    )
    _ = axs[i].set_title(shap_titles_list[i])

tmp_df = df.reset_index(drop=True)

X = tmp_df[features_list[2:]]
y = tmp_df[grouping_var]

accuracies_list = list()
all_pred_test_df = pd.DataFrame()
all_cors_df = pd.DataFrame()
kfold = RepeatedKFold(n_splits=10, n_repeats=10, random_state=42)
fold_number = 1

model = CatBoostRegressor(iterations=500,
                               depth=None,
                               learning_rate=1,
                               loss_function='RMSE',
                               verbose=False)

# enumerate the splits and summarize the distributions
for train_ix, test_ix in kfold.split(X):
    # select rows
    train_X, test_X = X.loc[train_ix, :], X.loc[test_ix, :]
    train_y, test_y = y.loc[train_ix], y.loc[test_ix]
    # summarize train and test composition
    train_0, train_1 = len(train_y[train_y==0]), len(train_y[train_y==1])
    test_0, test_1 = len(test_y[test_y==0]), len(test_y[test_y==1])
    
    _ = model.fit(X = train_X, 
                y = train_y,
               cat_features=X.columns.tolist())
    
    pred_y = model.predict(test_X)
    _ = accuracies_list.append(np.sqrt(mean_squared_error(test_y, pred_y)))
    
    pred_test_df = pd.DataFrame({grouping_var: test_y,
                 "predict": pred_y,
                                "fold_number": f"fold_{fold_number}"})
    
    all_pred_test_df = pd.concat([all_pred_test_df, 
                             pred_test_df
                            ])

    corr_df = pg.corr(x=pred_test_df[grouping_var], 
            y=pred_test_df["predict"], 
            alternative='two-sided', 
            method='spearman', 
           )
    
    all_cors_df = pd.concat([all_cors_df, 
                             corr_df.assign(fold_number=f"fold_{fold_number}")
                            ])
    
    fold_number += 1

_ = plt.figure(figsize=(3,5))
_ = sns.boxplot(y = accuracies_list)
_ = sns.swarmplot(y = accuracies_list, edgecolor="white", linewidth=1)
_ = plt.title("RMSE Cat Boost\nRegressor kfold cross validation")

pd.DataFrame(accuracies_list).describe().T

	count	mean	std	min	25%	50%	75%	max
0	100.0	1.460889	0.107116	1.172973	1.387053	1.45378	1.542548	1.713833

_ = sns.lmplot(data=all_pred_test_df, 
               x=grouping_var, 
               y="predict", 
               hue="fold_number",
              legend=False)

ax = sns.jointplot(data=all_pred_test_df, 
                  x=grouping_var, 
                  y="predict", 
                  hue="fold_number",
#                   kind="reg",
                   legend=False
                 )
# _ = ax._legend.remove()

all_cors_df.describe()

	n	r	p-val	power
count	100.000000	100.000000	1.000000e+02	100.000000
mean	227.200000	0.337624	1.474281e-04	0.989853
std	0.402015	0.063481	8.058543e-04	0.031872
min	227.000000	0.177255	1.944096e-15	0.766244
25%	227.000000	0.293919	4.683890e-09	0.995085
50%	227.000000	0.338370	1.743040e-07	0.999554
75%	227.000000	0.376328	6.681203e-06	0.999965
max	228.000000	0.495058	7.426213e-03	1.000000

_ = sns.boxplot(data=all_cors_df[["r", "p-val"]].melt(),
                x="variable", y="value")
_ = plt.axhline(y=0.05, c="grey", ls="--")

sort_shap_list = (pd.merge(shap_values_3
         .assign(shap_value=lambda d: d[["shap_value"]].abs())
         .groupby("variable")
         .mean()
         .sort_values(by="shap_value", ascending=False), 
         meta_df.loc[meta_df["New variable name"].isin(features_list), ["Item english translation", "New variable name"]],
         left_index=True,
         right_on="New variable name")
 .set_index(["New variable name", "Item english translation"])
 .index
 .tolist()
#  .drop("New variable name", axis=1)
)

pd.Series([x[1] for x in sort_shap_list])

0              If you got a coronavirus infection, how serious a threat would you rate it to your health?
1                                       In the indoors spaces I visit, people on the site think I should…
2     Taking a mask with you to a store or public transport, for example, has already become automatic...
3                                                                                       Using a face mask
4                                                        When I use a face mask, I feel or would feel ...
5                              Consequences of measures taken to prevent the spread of the coronavirus...
6     How likely do you think you would get a coronavirus infection in your free time in the next mont...
7     What consequences do you think it has if you use a face mask in your free time? If or when I use...
8     What consequences do you think it has if you use a face mask in your free time? If or when I use...
9                                                        When I use a face mask, I feel or would feel ...
10                                                                                            Ventilation
11                                                       When I use a face mask, I feel or would feel ...
12                                                           The fact that I would get infected myself ..
13    If two unvaccinated people from different households meet indoors, what means do you think would...
14                                                                That my loved one would get infected...
15    What consequences do you think it has if you use a face mask in your free time? If or when I use...
16    Putting on a mask, for example in a shop or on public transport, has already become automatic fo...
17    What consequences do you think it has if you use a face mask in your free time? If or when I use...
18    Who thinks you should use a face mask and who thinks not? In the following questions, by using a...
19                                                                       People at risk think I should ..
20    What consequences do you think it has if you use a face mask in your free time? If or when I use...
21                                                                                 Spread of coronavirus…
22                                                       When I use a face mask, I feel or would feel ...
23                                                                      The authorities think I should ..
24                                                                   Keeping a safety distance (2 meters)
25                                                       When I use a face mask, I feel or would feel ...
26      How likely do you think you will get a coronavirus infection in your free time in the next month?
dtype: object

def naive_catboost_shap(df: pd.DataFrame,
                                 grouping_var: str,
                                 column_list: list,
                                  plot_title: str,
                        max_display: int
                                 ):
    y = df[grouping_var]
    X = df[column_list]

    model = CatBoostRegressor(iterations=500,
                               depth=None,
                               learning_rate=1,
                               loss_function='RMSE',
                               verbose=False)

    # train the model
    _ = model.fit(X, y, cat_features=column_list)

    shap_values = model.get_feature_importance(Pool(X, label=y,cat_features=X.columns.tolist()), type="ShapValues")

    shap_values = shap_values[:,:-1]

    _ = shap.summary_plot(shap_values, 
                                  X.astype(int), 
                                  feature_names=X.columns, 
                                  max_display=max_display,
                                 show=False,
                         title=plot_title) 
    shap_plot = plt.gca()
    return shap_plot.get_figure()

display_length = 10

short_shap_plot_all = naive_catboost_shap(df = df,
                    grouping_var = grouping_var,
                    column_list = features_list,
                    plot_title="All",
                   max_display=display_length)

_ = short_shap_plot_all.gca().set_yticklabels(pd.Series([x[1] for x in sort_shap_list[:display_length]]).str.wrap(61).tolist(), fontsize=11)

short_shap_plot_all.set_figheight(8.5)
short_shap_plot_all.set_figwidth(12)

short_shap_plot_all

display_length = df.shape[0]

short_shap_plot_all = naive_catboost_shap(df = df,
                    grouping_var = grouping_var,
                    column_list = features_list,
                    plot_title="All",
                   max_display=display_length)

_ = short_shap_plot_all.gca().set_yticklabels(pd.Series([x[1] for x in sort_shap_list[:display_length]]).str.wrap(61).tolist(), fontsize=11)

short_shap_plot_all.set_figheight(30)
short_shap_plot_all.set_figwidth(12)

short_shap_plot_all